Image quality evaluations of image enhancement under various encoding rates for video surveillance system

2017 ◽  
Author(s):  
Airi Sakaushi ◽  
Kenji Kanai ◽  
Jiro Katto ◽  
Toshitaka Tsuda
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System

IP Video Surveillance System

2014 ◽  
pp. 264-289
Keyword(s):  
Image Quality ◽  
Video Surveillance ◽  
Motion Detection ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Light Sources ◽  
Hard Drives ◽  
Set Up ◽  
Definition Of ◽  
Network Camera

The chapter is a summary of IP surveillance systems: basic functions, the advantages of network video, customizing surveillance applications, and possible legal concerns. The most important step one can take before installing IP surveillance system is to define goals and requirements. Once these are determined, the video system can be set up. The required goals to be determined are the following: definition of the video surveillance system needs (installation plan, area of coverage, camera positioning, illumination conditions determination, camera cabling, the recording server positioning), network camera and/or video encoder selection (image quality, lens selection, network camera selection, Power over Ethernet [PoE], video motion detection, audio, accessories selection, testing), hardware (switches, additional light sources, power supplies, additional server for video management software, hard drives), software (software package selection, licenses, image quality and frame rate requirements, IP address range calculation, hard disk usage calculation, camera configuration, video motion detection settings, user access definition), and maintenance.

A Video Surveillance System for Monitoring the Endangered Mediterranean Monk Seal (Monachus monachus)

2007 ◽  
Vol 33 (2) ◽  
pp. 179-184 ◽  
Author(s):  
Panagiotis Dendrinos ◽  
Eleni Tounta ◽  
Alexandros A. Karamanlidis ◽  
Anastasios Legakis ◽  
Spyros Kotomatas
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System

scholarly journals Secure Video Surveillance Framework in Smart City

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi
Keyword(s):  
Video Surveillance ◽  
Smart City ◽  
Key Management ◽  
Surveillance System ◽  
Chinese Remainder Theorem ◽  
Smart Cities ◽  
Group Key Management ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Key Update

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

scholarly journals Smart Video Surveillance System Based on Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2958
Author(s):  
Antonio Carlos Cob-Parro ◽  
Cristina Losada-Gutiérrez ◽  
Marta Marrón-Romera ◽  
Alfredo Gardel-Vicente ◽  
Ignacio Bravo-Muñoz
Keyword(s):  
Computer Vision ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Smart Camera ◽  
People Detection ◽  
Camera System ◽  
Edge Node ◽  
New Processing ◽  
Processor Unit

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.

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